The pivot-based algorithm of Ailon et al. [1]} has been
revisited in terms of derandomization [2]},
parallelism [3]}, for classification
with asymmetric error costs [4]}, and for
clustering with categorical rather than binary relationships given
between elements [5]}, [6]}, to
name a few settings in which it has been applied and adapted.
A related objective function which maximizes the difference between
the satisfied and unsatisfied edges has been
studied [7]}, [8]}.
The
Correlation Clustering problem has also been studied in an online
setting [9]} and with respect to local
guarantees [10]}, [11]}, [12]}, [13]}.
Recent progress has lead to constant factor approximation
algorithms for the problem in the massively-parallel computation model [14]}, [15]}, in the streaming setting [16]}, online
setting [17]}, and with differential privacy
guarantees [18]}, [19]}, [20]}.
